CV Lattes
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ORCID
Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC) (Institutional affiliation for the last research proposal) Birthplace: Brazil
Full Professor at the Institute of Mathematical and Computer Sciences, University of São Paulo (ICMC-USP), Campus São Carlos, CNPq Research Productivity Fellow 1A and Vice-President of the Brazilian Computer Society (SBC). He is coordinator of the IARA network, Artificial Intelligence Recreating Environments. He was a member of the CNPq Computer Science Advisory Committee (CA-CC), from 2018 to 2021 (coordinator from 2019 to 2020). From 2013 to 2017, he was member of the board of the International Association for Statistical Computing (IASC), of the International Institute of Statistics. He holds a Bachelor's degree (1987) and a Master's Degree in Computer Science (1990) from the Federal University of Pernambuco, and a PhD in Electronic Engineering from the University of Kent (1994). His main research interests are Machine Learning, Data Mining and Data Science, with applications in various areas. Author of several books, including Inteligência Artificial: Uma abordagem de Aprendizado de Máquina, published by GrupoGen in 2011 and Jabuti Award in 2012, and A General Introduction to Data Analytics, published by Wiley, in 2018. Evaluates projects for national research funding agencies, CAPES, CNPq, FAPESP, FACEPE, FAPEMIG and Fundação Serrapilheira, and international, NSERC (Canada), The Leverhulme Trust (UK), EPSRC UK), GACR (Czech Republic), CONICYT (Chile), Croatian Science Foundation (Croatia) and MIUR (Italy). He is Dean of ICMC-USP and the Center for Applied Mathematical Sciences for Industry at USP. He was member of the CNPq Computer Science Advisory Committee (CA-CC), of the Consultative Council of the MCTI-EMBRAPII network on Innovation in Artificial Intelligence, of the Science for Education Network (CpE), of the International Network for Government Science Advice (INGSA), of the University Council of the Universidade de São Paulo, of the Advanced Institute for Artificial Intelligence (AI2) and of the Strategy and Partnerships Board of the UKRI Centre for Doctoral Training in Accountable, Responsible and Transparent AI (ART-AI), at University of Bath, UK. Associate member of the Artificial Intelligence Institute - Te Ipu o te Mahara of the University of Waikato, New Zealand and of the Governance Group of the Brazilian Strategy of Artificial Intelligence (EBIA) of the Ministry of Science, Technology and Innovations, of the Technological Council of the Fund Management Association Equity Chronos (?CHRONOS?) and of the Governance Committee of Geographical Indications (IG) São Carlos. He was member of the Technical Scientific Council of the School of Applied Mathematics of the Getúlio Vargas Foundation (EMAp-FGV) from 2018 to 2020. (Source: Lattes Curriculum)
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Mobility plays a central role in various related activities we conduct, especially in work, commerce, industry and leisure. The study of mobility for different purposes (e.g., economics, public services, academic study) should be done in a systematic way since from the scientific point of view it is the most fundamental issue that must be studied, so proper solutions can be proposed. Thus…
The focus of this proposal is the transfer of mathematical knowledge to other areas of science, technology and industry, through a structured research center specially tailored for this purpose. All mathematical knowledge is ultimately applicable, if not directly, through other areas of knowledge. In some areas the application of mathematics is almost immediate. However, the practical imp…
Without being aware, we are using technologies based on Machine learning (ML) algorithms in a growing number of our daily activities. The use of ML has made many risk and tiring activities safer, more reliable and more accurate. Despite these contributions, new demands require the development of new ML algorithms, or use of these algorithms in new and innovative ways. Two important curren…
This international cooperation project describes the activities to be performed by Prof. Dr. Carlos M. M. O. P. Soares, from the School of Engineering of University of Porto and INESC TEC, during his visit to ICMC-USP. In this visit he will work in a scientific cooperation in the field of Data Mining, specifically in Meta-learning. Prof. Carlos Soares will also present a research seminar …
(Only some records are available in English at this moment)
The generation of highly realistic urban environments and artificial cities is of great interest to the entertainment industry as well as for the creation of true-to-life simulation sceneries on the lack of real-world data. Being based on a set of features found in real cities, the methods for the said purpose more often than not show the capability of replicating properties such as zonin…
The amount of available data has increased in large amounts in past years. The development of new technologies, and fast and cheap ways of communicating have promoted this phenomena. Traditional machine learning approaches, i.e. batch learning, became inadequate to scenarios where potentially endless flows of information may exist. In this sense, new techniques were developed to deal with…
When it was discovered that at least 80% of the human genome is biologically active, the non-coding content of the human genome was no longer viewed solely as "junk" DNA. Non-coding sequences have eventually gained increasing space in recent research for important roles involving physiological processes in organisms. Small non-coding RNAs then gain a greater relevance to fundamentally und…
Sentiment Analysis is a fundamental task for understanding people's opinions regarding subjects, people or products, allowing to improve processes in different social and commercial segments. With the use of Machine Learning, it was possible to process a massive amount of data and achieve promising results. However, the models used, in their majority, are considered black box models, beca…
(Only some records are available in English at this moment)
Different microbial communities inhabit the different regions of the human body, whether mouth, gut, vagina, or others. They are collectively called the Human Microbiome (HM). These communities participate in essential processes in human health. Thus, a better understanding of the human core microbiome may help early diagnosis of disorders and contribute to potentially more efficient trea…
Forecasting is one of the main tasks of time series analysis. Selecting forecasting models is a crucial decision-making step in many real-world applications. To obtain forecasts that best explain the variables of interest in the future, it is necessary to select the model that best fits a given time series. However, the selection task can be quite computationally expensive due to many ava…
Nearest Neighbor Search (NNS) is one of the pivotal research areas in computer science.Machine learning leverages efficient NNS algorithms to speed up distance-based algorithms.However, most online nearest neighbor-based learning algorithms still rely on brute-force search strategies to this day.This research project is primarily concerned with the sliding window-based online k-Nearest Ne…
Deep learning (DL), a class of machine learning algorithms, has demonstrated a high capacity to induce models in different knowledge domains. DL models are today considered state of the art in many applications, such as image and voice recognition, simulation of game players, among others. While DL models have presented high predictive accuracy in many real-world tasks, little is understo…
Several works have investigated the effect of data imbalance on the performance of predictive models. Classification tasks using imbalanced data are not challenging on their own. When the classes are linearly separable, a regular classification algorithm usually induces predictive models able to distinguish the classes properly. Imbalance data poses a difficulty for the minority class whe…
In many practical classification problems, the data set used for the induction of a classifier is significantly unbalanced. This occurs when the number of instances of a particular class is much lower than the number of instances in the other(s) class(es). Unbalanced data sets can compromise the performance of most classical classification algorithms, since these algorithms assume a balan…
(Only some records are available in English at this moment)
| 2 / 2 | Ongoing research grants |
| 25 / 17 | Completed research grants |
| 2 / 2 | Ongoing scholarships in Brazil |
| 67 / 41 | Completed scholarships in Brazil |
| 3 / 3 | Ongoing scholarships abroad |
| 11 / 11 | Completed scholarships abroad |
| 110 / 76 | All research grants and scholarships |
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